Covid cases

Introduction

Our objective is to see how to create a document using R.

The document will combine: - text - code - R output

The document will be in html, so we can use both as a text document and for presentation.

Specialized formats are also available, but this is a flexible method.

Types of commonly used files

• .csv for data
• .R for R script, writing commands that can be clicked
• .Rmd (R notebook) (will produce html)
• .html (can be viewed in a web browser like Google chrome)

Data

owid_ies <- read.csv("~/Documents/R/math_R/owid_ies.csv")

Data is from the Our World in Data website, publicly available and easily downloadable.

We look at the structure of the data with str().

str(owid_ies)

## 'data.frame':    539 obs. of  6 variables:
##  $ iso_code                       : chr  "BGD" "BGD" "BGD" "BGD" ...
##  $ continent                      : chr  "Asia" "Asia" "Asia" "Asia" ...
##  $ location                       : chr  "Bangladesh" "Bangladesh" "Bangladesh" "Bangladesh" ...
##  $ date                           : chr  "2020-03-03" "2020-03-09" "2020-03-15" "2020-03-21" ...
##  $ new_cases_smoothed_per_million : num  NA NA 0.002 0.019 0.024 ...
##  $ new_deaths_smoothed_per_million: num  NA NA 0 0.002 0.003 0.001 0.012 0.025 0.054 0.047 ...

We change location variable to a factor.

owid_ies$location <- factor(owid_ies$location)
str(owid_ies$location)

##  Factor w/ 6 levels "Bangladesh","Brazil",..: 1 1 1 1 1 1 1 1 1 1 ...

We load packages.

library(ggplot2) # for plotting
library(dplyr) # for working with data

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(scales) # for working with scales
library(lubridate) # for dates

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(ggthemes) # for themes of graph

We work with dates.

owid_ies$date <- ymd(owid_ies$date)
datebreaks <- seq(as.Date("2020-01-01"),
                  as.Date("2021-07-01"),
                  by = "6 month")

We plot the figure.

gg3 <- 
  ggplot(owid_ies, aes(x = date,
            y = new_cases_smoothed_per_million,
            group = location)) +
  geom_line() + 
  facet_wrap(~location, scales = "free_y",
             ncol = 2) +
  scale_x_date(breaks = datebreaks,
               labels = date_format("%Y %b")) +
    theme(axis.text.x = element_text(
      angle = 30)) +
  xlab("") +
  ggtitle("COVID-19 cases") +
  theme_fivethirtyeight(base_size = 9)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

ggplotly(gg3)

Discussion

Different countries experienced COVID-19 in waves.